Update app.py

app.py

@@ -19,6 +19,7 @@ from pydantic import BaseModel
 from dotenv import load_dotenv
 from datetime import datetime
 import logging
+from tqdm import tqdm  # Importar tqdm
 
 # Configuración de logging
 logging.basicConfig(level=logging.INFO)
@@ -95,7 +96,7 @@ async def train_and_save_model():
 
         training = []
         output_empty = [0] * len(classes)
-        for doc in documents:
+        for doc in tqdm(documents, desc="Preparando datos"):
             bag = [0] * len(words)
             pattern_words = [lemmatizer.lemmatize(word.lower()) for word in doc[0]]
             for w in words:
@@ -135,6 +136,7 @@ async def train_and_save_model():
             sgd = SGD(learning_rate=0.01, momentum=0.9, nesterov=True)
             model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
 
+        print("Entrenando el modelo...")
         model.fit(train_x, train_y, epochs=1, batch_size=len(train_x), verbose=0)
 
         r.set('words', pickle.dumps(words))
@@ -257,19 +259,21 @@ html_code = """
     function sendMessage() {
         let userInput = document.getElementById('user_input').value;
         document.getElementById('user_input').value = '';
+        document.getElementById('chatbox').innerHTML += '<p><b>Tú:</b> ' + userInput + '</p>';
+        
         fetch('/chat', {
             method: 'POST',
-            headers: {'Content-Type': 'application/json'},
-            body: JSON.stringify({"message": userInput})
+            headers: {
+                'Content-Type': 'application/json'
+            },
+            body: JSON.stringify({ message: userInput })
         })
         .then(response => response.json())
         .then(data => {
-            let chatbox = document.getElementById('chatbox');
-            chatbox.innerHTML += '<p><b>Tú:</b> ' + userInput + '</p>';
             data.forEach(response => {
-                chatbox.innerHTML += '<p><b>Bot:</b> ' + response.intent + ' (' + response.probability + ')</p>';
+                document.getElementById('chatbox').innerHTML += '<p><b>Bot:</b> ' + response.intent + ' (' + response.probability + ')</p>';
             });
-            chatbox.scrollTop = chatbox.scrollHeight;
+            document.getElementById('chatbox').scrollTop = document.getElementById('chatbox').scrollHeight;
         });
     }
     </script>
@@ -287,4 +291,4 @@ if __name__ == "__main__":
     nltk.download('omw-1.4')
     nltk.download('averaged_perceptron_tagger')
     nltk.download('punkt_tab')
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    uvicorn.run(app, host="0.0.0.0", port=8000)